Automated highway tag assessment of openstreetmap road networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

OpenStreetMap (OSM) has been demonstrated to be a valu-able source of spatial data in the context of many applica-tions. However concerns still exist regarding the quality of such data and this has limited the proliferation of its use. Consequently much research has been invested in the devel-opment of methods for assessing and/or improving the qual-ity of OSM data. However most of these methods require ground-truth data, which, in many cases, may not be avail-able. In this paper we present a novel solution for OSM data quality assessment that does not require ground-truth data. We consider the semantic accuracy of OSM street network data, and in particular, the associated semantic class (road class) information. A machine learning model is proposed that learns the geometrical and topological characteristics of different semantic classes of streets. This model is sub-sequently used to accurately determine if a street has been assigned a correct/incorrect semantic class.

Original languageEnglish
Title of host publication22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2014
EditorsMarkus Schneider, Michael Gertz, Yan Huang, Jagan Sankaranarayanan, John Krumm
PublisherAssociation for Computing Machinery (ACM)
Pages449-452
Number of pages4
ISBN (Electronic)9781450331319
DOIs
Publication statusPublished - 4 Nov 2014
Externally publishedYes
Event22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2014 - Dallas, United States
Duration: 4 Nov 20147 Nov 2014

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Volume04-07-November-2014

Conference

Conference22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2014
Country/TerritoryUnited States
CityDallas
Period4/11/147/11/14

Keywords

  • Data Quality
  • Ma-chine Learning
  • OpenStreetMap
  • Street Network Analysis

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